Efficient detection algorithm of chemical warfare agents for FTIR-based hyperspectral imagery using SVM classifier

Hyeong-Geun Yu; Jae-Hoon Lee; Dong-Jo Park; Hyeon-Woo Nam; Byeong-Hwang Park

doi:10.1117/12.2322027

16 November 2018 Efficient detection algorithm of chemical warfare agents for FTIR-based hyperspectral imagery using SVM classifier

Hyeong-Geun Yu, Jae-Hoon Lee, Dong-Jo Park, Hyeon-Woo Nam, Byeong-Hwang Park

Author Affiliations +

Proceedings Volume 10768, Imaging Spectrometry XXII: Applications, Sensors, and Processing; 107680H (2018) https://doi.org/10.1117/12.2322027
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

The hyperspectral imaging system (HIS) using a Fourier transform infrared (FTIR) spectrometer is one of the key technologies for detection and identification of chemical warfare agents (CWAs). Recently, various detection algorithms based on machine learning techniques have been studied. These algorithms are robust against performance degradation caused by noise signatures generated by FTIR instruments. However, interference signatures from background materials degrade detection performance. In this paper, we propose an efficient algorithm that uses a support vector machine (SVM) classifier to detect CWAs. In contrast to the conventional algorithms that use measured spectra to train the SVM classifier, the proposed algorithm trains the SVM classifier using CWA signatures obtained by removing background signatures from measured spectra. Therefore, the proposed algorithm is robust against the performance degradation induced by interference signatures from background materials. Experimental results verify that the algorithm can detect CWA clouds effectively.

Conference Presentation

Citation Download Citation

Hyeong-Geun Yu, Jae-Hoon Lee, Dong-Jo Park, Hyeon-Woo Nam, and Byeong-Hwang Park "Efficient detection algorithm of chemical warfare agents for FTIR-based hyperspectral imagery using SVM classifier", Proc. SPIE 10768, Imaging Spectrometry XXII: Applications, Sensors, and Processing, 107680H (16 November 2018); https://doi.org/10.1117/12.2322027

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